---
title: "CB2 Tutorial"
author: "Hyun-Hwan Jeong"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{CB2 Tutorial}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


CRISPRBetaBinomial (CB<sup>2</sup>) is a package for designing a statistical hypothesis test for robust target identification, developing an accurate mapping algorithm to quantify sgRNA abundances, and minimizing the parameters necessary for CRISPR pooled screen data analysis. This document shows how to use CB2 for the CRISPR pooled screen data analysis.

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

First, import `CB2` package using `library(),` and it will be helpful to import other packages as follows:

```{r}
library(CB2)
library(magrittr)
library(glue)
library(tibble)
library(dplyr)
library(ggplot2)
```

There are three different basic functions in CB<sup>2</sup>. The first function provides a quantification of counts of sgRNAs from the NGS samples. It requires a library file (`.fasta` or `.fa`) and a list of samples (`.fastq`). The library file must contain an annotation of sgRNAs in the library used in the screen. A sgRNA annotation consists of a barcode sequence (20nt sequence where sgRNA would target) and a name of a gene which the sgRNA suppose to target. 

Here is an example of the loading data for the screen analysis. Files in the example are contained in the `CB2` package. 

```{r}
# load the file path of the annotation file.
FASTA <- system.file("extdata", "toydata",
                     "small_sample.fasta",
                     package = "CB2")
system("tail -6 {FASTA}" %>% glue)
```

The first two lines of the annotation file indicate the annotation of the first sgRNA, and the next two lines are the annotation of the second sgRNA, and so on. The first line of an annotation is formatted as `><genename>_<id>`, where `<genename>` is an id of a symbol of the target gene for the sgRNA and `<id>` is the unique identifier for the sgRNA. `><genename>_<id>` is the completed identifier of the sgRNA and a completed identifier should not appear more than once. The second line of an annotation is the 20nt sequence, and it indicates which part of the target gene will be targeted by the sgRNA. 

The first annotation indicates the library contains a sgRNA and `RAB_3` is the identifier of the sgRNA. This sgRNA is supposed to target `RAB` gene and the intended target locus is `CTGTAGAAGCTACATCGGCT`

We also have an example of the NGS sample file. The following snippet will display the contents of an NGS sample file.

```{r}
FASTQ <- system.file("extdata", "toydata",
                     "Base1.fastq",
                     package = "CB2")
system("head -8 {FASTQ}" %>% glue)
```

The NGS sample file contains multiple reads, and each read consists of four sequential lines. The first line is the id of the reads, and the second line includes a sequence of the read, and we assume that a read contains a nucleotide sequence of the sgRNA as a substring of the read. The third line only includes '+' and the last line includes the quality of each nucleotide of the read ([Phread quality score](https://en.wikipedia.org/wiki/FASTQ_format)).


Let's get start the analysis. Before running the analysis we will see the list of `FASTQ` files we can use from the `toydata` example.

```{r}
ex_path <- system.file("extdata", "toydata", package = "CB2")
Sys.glob("{ex_path}/*.fastq" %>% glue) %>% basename()
```

From the example directly above, we can recognize there are three groups  (Base, High, and Low) in the example data, and each of them has two replicates. We will perform an analysis between `Base` and `High.` The first thing we need to do is creating a design matrix. The below code shows how to build it.

```{r}
df_design <- tribble(
  ~group, ~sample_name,
  "Base", "Base1",  
  "Base", "Base2", 
  "High", "High1",
  "High", "High2"
) %>% mutate(
  fastq_path = glue("{ex_path}/{sample_name}.fastq")
)

df_design
```

`df_design` contains three columns and each row contains information of a sample. The first column is `group` where the sample belongs to, and the `sample_name` is the name of the sample for a convenience. `fastq_path` is the place where you will have the NGS sample file.

After creating the `df_design,` and we can run a sgRNA quantification by calling `run_sgrna_quant.`

```{r}
cb2_count <- run_sgrna_quant(FASTA, df_design)
```

```{r}
head(cb2_count$count)
```

After running `run_sgrna_quant,` we will have a data frame (`cb2_count$count`) and a numeric vector (`cb2_count$total`). The data frame contains sgRNA counts for each sample, and the numeric vector contains the number of reads for each sample. 
In the data frame, each row corresponds to a sgRNA and each column belongs to a sample. Each value in the data frame indicates read counts of the corresponded sgRNA and sample, and it implies how many reads have been aligned to the sgRNA from the sample file. We assume the number will be used to approximate the number of knock-out cells of the target gene of the sgRNA.

```{r}
head(cb2_count$total)
```

We are also able to lookup CPM (Count Per Million mapped read counts) using `get_CPM.`

```{r}
get_CPM(cb2_count$count)
```

There are four functions we can use to check the quality of the input data.  The first function (`plot_count_distribution`) will give the mappability (the success rate of sgRNA identification from reads) for each sample.

```{r}
plot_count_distribution(cb2_count$count %>% get_CPM, df_design, add_dots = T)
```

We can also check the mappability (The proportion of the number of reads successfully aligned to a sgRNA in the library among the entire reads) using `calc_mappability` function.

```{r}
calc_mappability(cb2_count, df_design)
```

`plot_PCA` can be a way of checking data quality.

```{r}
plot_PCA(cb2_count$count %>% get_CPM, df_design)
```

The last function (`plot_corr_heatmap`) display a sgRNA-level correlation heatmap of NGS samples. We assume that samples in the same group clustered together if the data quality is good.

```{r}
plot_corr_heatmap(cb2_count$count %>% get_CPM, df_design)
```


After we find the data quality is good to move to the next step, then we can perform an analysis for a sgRNA-level using `measure_sgrna_stats`

```{r}
sgrna_stat <- measure_sgrna_stats(cb2_count$count, df_design, "High", "Base")
sgrna_stat
```

As you can see above, we need four different parameters for the function. The first is a matrix of the read count, and the second parameter is the design data frame. The last two are the groups we are interested in performing differential abundance test for each sgRNA.

Here is the information of each column in the data.frame of the sgRNA-level statistics:

* `sgRNA`: The sgRNA identifier.
* `gene`: The gene is the target of the sgRNA 
* `n_a`: The number of replicates of the first group.
* `n_b`: The number of replicates of the second group.
* `phat_a`: The proportion value of the sgRNA for the first group.
* `phat_b`: The proportion value of the sgRNA for the second group.
* `vhat_a`: The variance of the sgRNA for the first group.
* `vhat_b`: The variance of the sgRNA for the second group.
* `cpm_a`: The mean CPM of the sgRNA within the first group.
* `cpm_b`: The mean CPM of the sgRNA within the second group.
* `logFC`: The log fold change of sgRNA between two groups, and calculated by $log_{2}\frac{CPM_{a}+1}{CPM_{b}+1}$
* `t_value`: The value for the t-statistics.
* `df`: The value of the degree of freedom, and will be used to calculate the p-value of the sgRNA.
* `p_ts`: The p-value indicates a difference between the two groups.
* `p_pa`: The p-value indicates enrichment of the first group.
* `p_pb`: The p-value indicates enrichment of the second group.
* `fdr_ts`: The adjusted P-value of `p_ts`.
* `fdr_pa`: The adjusted P-value of `p_pa`.
* `fdr_pb`: The adjusted P-value of `p_pb`.

Once we finish the sgRNA-level test, we can perform a gene-level test using `measure_gene_stats.`

```{r}
gene_stats <- measure_gene_stats(sgrna_stat)
gene_stats
```

Here is the information of each column in the data.frame of the gene-level statistics:

* `gene`: The gene name to be tested.
* `n_sgrna`: The number of sgRNA targets the gene in the library.
* `cpm_a`: The mean of CPM of sgRNAs within the first group.
* `cpm_b`: The mean of CPM of sgRNAs within the second group.
* `logFC`: The log fold change of the gene between two groups, and calculated by $log_{2}\frac{CPM_{a}+1}{CPM_{b}+1}$
* `p_ts`: The p-value indicates a difference between the two groups at the gene-level.
* `p_pa`: The p-value indicates enrichment of the first group at the gene-level.
* `p_pb`: The p-value indicates enrichment of the second group at the gene-level.
* `fdr_ts`: The adjusted P-value of `p_ts`.
* `fdr_pa`: The adjusted P-value of `p_pa`.
* `fdr_pb`: The adjusted P-value of `p_pb`.

After we have a result of the gene-level test, we can filter out a list of genes using different measures. For example, if you are considering to find genes has a differential abundance between two groups, you can use the value `fdr_ts` for the hit selection. If you want to see some genes has enrichment of abundance in the first group (i.e., depiction in the opposite group), you lookup `fdr_pa` value, and `fdr_pb` can be used to see an enrichment of abundance in the second group. Here, we use `fdr_ts` to identify the hit genes.

```{r}
gene_stats %>% 
  filter(fdr_ts < 0.1)
```

CB<sup>2</sup> also supports a useful dot plot function to lookup the read counts for a gene, and this function can be used to clarify an interesting hit is valid.

```{r}
plot_dotplot(cb2_count$count, df_design, "PARK2")
```